Overview

Dataset statistics

Number of variables23
Number of observations721
Missing cells978
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory114.9 KiB
Average record size in memory163.2 B

Variable types

Numeric13
Categorical7
Boolean3

Alerts

Name has a high cardinality: 721 distinct values High cardinality
Number is highly correlated with Type_2 and 2 other fieldsHigh correlation
Total is highly correlated with HP and 10 other fieldsHigh correlation
HP is highly correlated with Total and 5 other fieldsHigh correlation
Attack is highly correlated with Total and 4 other fieldsHigh correlation
Defense is highly correlated with Total and 4 other fieldsHigh correlation
Sp_Atk is highly correlated with Total and 5 other fieldsHigh correlation
Sp_Def is highly correlated with Total and 2 other fieldsHigh correlation
Speed is highly correlated with Total and 1 other fieldsHigh correlation
Generation is highly correlated with Number and 1 other fieldsHigh correlation
Pr_Male is highly correlated with Type_1 and 2 other fieldsHigh correlation
Height_m is highly correlated with Total and 2 other fieldsHigh correlation
Weight_kg is highly correlated with HP and 1 other fieldsHigh correlation
Catch_Rate is highly correlated with Total and 6 other fieldsHigh correlation
isLegendary is highly correlated with Total and 4 other fieldsHigh correlation
hasGender is highly correlated with Total and 4 other fieldsHigh correlation
Egg_Group_1 is highly correlated with Type_1 and 9 other fieldsHigh correlation
Egg_Group_2 is highly correlated with Number and 5 other fieldsHigh correlation
Type_1 is highly correlated with Type_2 and 5 other fieldsHigh correlation
Type_2 is highly correlated with Number and 7 other fieldsHigh correlation
Color is highly correlated with Type_1 and 3 other fieldsHigh correlation
Body_Style is highly correlated with Type_1 and 3 other fieldsHigh correlation
Type_2 has 371 (51.5%) missing values Missing
Pr_Male has 77 (10.7%) missing values Missing
Egg_Group_2 has 530 (73.5%) missing values Missing
Number is uniformly distributed Uniform
Name is uniformly distributed Uniform
Number has unique values Unique
Name has unique values Unique
Pr_Male has 23 (3.2%) zeros Zeros

Reproduction

Analysis started2022-11-14 01:12:52.766023
Analysis finished2022-11-14 01:13:10.055021
Duration17.29 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Number
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct721
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361
Minimum1
Maximum721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:10.347023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1181
median361
Q3541
95-th percentile685
Maximum721
Range720
Interquartile range (IQR)360

Descriptive statistics

Standard deviation208.2790596
Coefficient of variation (CV)0.5769503036
Kurtosis-1.2
Mean361
Median Absolute Deviation (MAD)180
Skewness0
Sum260281
Variance43380.16667
MonotonicityStrictly increasing
2022-11-13T20:13:10.445024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
4751
 
0.1%
4771
 
0.1%
4781
 
0.1%
4791
 
0.1%
4801
 
0.1%
4811
 
0.1%
4821
 
0.1%
4831
 
0.1%
4841
 
0.1%
Other values (711)711
98.6%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
7211
0.1%
7201
0.1%
7191
0.1%
7181
0.1%
7171
0.1%
7161
0.1%
7151
0.1%
7141
0.1%
7131
0.1%
7121
0.1%

Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct721
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Bulbasaur
 
1
Gallade
 
1
Dusknoir
 
1
Froslass
 
1
Rotom
 
1
Other values (716)
716 

Length

Max length11
Median length10
Mean length7.404993065
Min length3

Characters and Unicode

Total characters5339
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique721 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowCharmander
5th rowCharmeleon

Common Values

ValueCountFrequency (%)
Bulbasaur1
 
0.1%
Gallade1
 
0.1%
Dusknoir1
 
0.1%
Froslass1
 
0.1%
Rotom1
 
0.1%
Uxie1
 
0.1%
Mesprit1
 
0.1%
Azelf1
 
0.1%
Dialga1
 
0.1%
Palkia1
 
0.1%
Other values (711)711
98.6%

Length

2022-11-13T20:13:10.536522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bulbasaur1
 
0.1%
ekans1
 
0.1%
metapod1
 
0.1%
venusaur1
 
0.1%
charmander1
 
0.1%
charmeleon1
 
0.1%
charizard1
 
0.1%
squirtle1
 
0.1%
wartortle1
 
0.1%
blastoise1
 
0.1%
Other values (711)711
98.6%

Most occurring characters

ValueCountFrequency (%)
a481
 
9.0%
e449
 
8.4%
o434
 
8.1%
i391
 
7.3%
r390
 
7.3%
l314
 
5.9%
n313
 
5.9%
t253
 
4.7%
u210
 
3.9%
s173
 
3.2%
Other values (50)1931
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4604
86.2%
Uppercase Letter725
 
13.6%
Other Punctuation3
 
0.1%
Dash Punctuation2
 
< 0.1%
Connector Punctuation2
 
< 0.1%
Other Symbol2
 
< 0.1%
Decimal Number1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a481
10.4%
e449
 
9.8%
o434
 
9.4%
i391
 
8.5%
r390
 
8.5%
l314
 
6.8%
n313
 
6.8%
t253
 
5.5%
u210
 
4.6%
s173
 
3.8%
Other values (17)1196
26.0%
Uppercase Letter
ValueCountFrequency (%)
S103
14.2%
M60
 
8.3%
C55
 
7.6%
P48
 
6.6%
G47
 
6.5%
D41
 
5.7%
B39
 
5.4%
T37
 
5.1%
L34
 
4.7%
A33
 
4.6%
Other values (16)228
31.4%
Other Punctuation
ValueCountFrequency (%)
.2
66.7%
'1
33.3%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Decimal Number
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5329
99.8%
Common10
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a481
 
9.0%
e449
 
8.4%
o434
 
8.1%
i391
 
7.3%
r390
 
7.3%
l314
 
5.9%
n313
 
5.9%
t253
 
4.7%
u210
 
3.9%
s173
 
3.2%
Other values (43)1921
36.0%
Common
ValueCountFrequency (%)
-2
20.0%
_2
20.0%
.2
20.0%
1
10.0%
1
10.0%
'1
10.0%
21
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5335
99.9%
None2
 
< 0.1%
Misc Symbols2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a481
 
9.0%
e449
 
8.4%
o434
 
8.1%
i391
 
7.3%
r390
 
7.3%
l314
 
5.9%
n313
 
5.9%
t253
 
4.7%
u210
 
3.9%
s173
 
3.2%
Other values (47)1927
36.1%
None
ValueCountFrequency (%)
é2
100.0%
Misc Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%

Type_1
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Water
105 
Normal
93 
Grass
66 
Bug
63 
Fire
47 
Other values (13)
347 

Length

Max length8
Median length7
Mean length5.231622746
Min length3

Characters and Unicode

Total characters3772
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowFire
5th rowFire

Common Values

ValueCountFrequency (%)
Water105
14.6%
Normal93
12.9%
Grass66
 
9.2%
Bug63
 
8.7%
Fire47
 
6.5%
Psychic47
 
6.5%
Rock41
 
5.7%
Electric36
 
5.0%
Ground30
 
4.2%
Poison28
 
3.9%
Other values (8)165
22.9%

Length

2022-11-13T20:13:10.607024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water105
14.6%
normal93
12.9%
grass66
 
9.2%
bug63
 
8.7%
fire47
 
6.5%
psychic47
 
6.5%
rock41
 
5.7%
electric36
 
5.0%
ground30
 
4.2%
dark28
 
3.9%
Other values (8)165
22.9%

Most occurring characters

ValueCountFrequency (%)
r446
 
11.8%
a333
 
8.8%
o267
 
7.1%
e255
 
6.8%
c230
 
6.1%
s230
 
6.1%
i228
 
6.0%
t211
 
5.6%
l154
 
4.1%
g140
 
3.7%
Other values (18)1278
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3051
80.9%
Uppercase Letter721
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r446
14.6%
a333
10.9%
o267
8.8%
e255
8.4%
c230
7.5%
s230
7.5%
i228
7.5%
t211
 
6.9%
l154
 
5.0%
g140
 
4.6%
Other values (7)557
18.3%
Uppercase Letter
ValueCountFrequency (%)
G119
16.5%
W105
14.6%
N93
12.9%
F92
12.8%
P75
10.4%
B63
8.7%
D52
7.2%
R41
 
5.7%
E36
 
5.0%
I23
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3772
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r446
 
11.8%
a333
 
8.8%
o267
 
7.1%
e255
 
6.8%
c230
 
6.1%
s230
 
6.1%
i228
 
6.0%
t211
 
5.6%
l154
 
4.1%
g140
 
3.7%
Other values (18)1278
33.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r446
 
11.8%
a333
 
8.8%
o267
 
7.1%
e255
 
6.8%
c230
 
6.1%
s230
 
6.1%
i228
 
6.0%
t211
 
5.6%
l154
 
4.1%
g140
 
3.7%
Other values (18)1278
33.9%

Type_2
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)5.1%
Missing371
Missing (%)51.5%
Memory size5.8 KiB
Flying
87 
Poison
31 
Ground
30 
Psychic
27 
Fighting
19 
Other values (13)
156 

Length

Max length8
Median length7
Mean length5.657142857
Min length3

Characters and Unicode

Total characters1980
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoison
2nd rowPoison
3rd rowPoison
4th rowFlying
5th rowFlying

Common Values

ValueCountFrequency (%)
Flying87
 
12.1%
Poison31
 
4.3%
Ground30
 
4.2%
Psychic27
 
3.7%
Fighting19
 
2.6%
Steel19
 
2.6%
Fairy18
 
2.5%
Grass18
 
2.5%
Dark16
 
2.2%
Dragon14
 
1.9%
Other values (8)71
 
9.8%
(Missing)371
51.5%

Length

2022-11-13T20:13:10.672023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying87
24.9%
poison31
 
8.9%
ground30
 
8.6%
psychic27
 
7.7%
fighting19
 
5.4%
steel19
 
5.4%
fairy18
 
5.1%
grass18
 
5.1%
dark16
 
4.6%
rock14
 
4.0%
Other values (8)71
20.3%

Most occurring characters

ValueCountFrequency (%)
i216
 
10.9%
n181
 
9.1%
g142
 
7.2%
o136
 
6.9%
F133
 
6.7%
y132
 
6.7%
r128
 
6.5%
l116
 
5.9%
s106
 
5.4%
c90
 
4.5%
Other values (18)600
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1630
82.3%
Uppercase Letter350
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i216
13.3%
n181
11.1%
g142
8.7%
o136
8.3%
y132
8.1%
r128
7.9%
l116
 
7.1%
s106
 
6.5%
c90
 
5.5%
a83
 
5.1%
Other values (7)300
18.4%
Uppercase Letter
ValueCountFrequency (%)
F133
38.0%
G60
17.1%
P58
16.6%
D30
 
8.6%
S19
 
5.4%
R14
 
4.0%
W13
 
3.7%
I10
 
2.9%
E6
 
1.7%
N4
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin1980
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i216
 
10.9%
n181
 
9.1%
g142
 
7.2%
o136
 
6.9%
F133
 
6.7%
y132
 
6.7%
r128
 
6.5%
l116
 
5.9%
s106
 
5.4%
c90
 
4.5%
Other values (18)600
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i216
 
10.9%
n181
 
9.1%
g142
 
7.2%
o136
 
6.9%
F133
 
6.7%
y132
 
6.7%
r128
 
6.5%
l116
 
5.9%
s106
 
5.4%
c90
 
4.5%
Other values (18)600
30.3%

Total
Real number (ℝ≥0)

HIGH CORRELATION

Distinct183
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean417.9459085
Minimum180
Maximum720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:10.743526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1320
median424
Q3499
95-th percentile600
Maximum720
Range540
Interquartile range (IQR)179

Descriptive statistics

Standard deviation109.6636707
Coefficient of variation (CV)0.2623872337
Kurtosis-0.6429150365
Mean417.9459085
Median Absolute Deviation (MAD)86
Skewness0.06165000903
Sum301339
Variance12026.12068
MonotonicityNot monotonic
2022-11-13T20:13:10.831023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40526
 
3.6%
60026
 
3.6%
50023
 
3.2%
30019
 
2.6%
58018
 
2.5%
49018
 
2.5%
52516
 
2.2%
33015
 
2.1%
49514
 
1.9%
48013
 
1.8%
Other values (173)533
73.9%
ValueCountFrequency (%)
1801
 
0.1%
1901
 
0.1%
1941
 
0.1%
1953
0.4%
1981
 
0.1%
2003
0.4%
2055
0.7%
2103
0.4%
2131
 
0.1%
2151
 
0.1%
ValueCountFrequency (%)
7201
 
0.1%
68011
1.5%
6704
 
0.6%
6601
 
0.1%
60026
3.6%
58018
2.5%
5671
 
0.1%
5551
 
0.1%
5521
 
0.1%
5501
 
0.1%

HP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct94
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.38002774
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:10.914027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.84827182
Coefficient of variation (CV)0.3780090865
Kurtosis7.780695862
Mean68.38002774
Median Absolute Deviation (MAD)15
Skewness1.669208681
Sum49302
Variance668.1331561
MonotonicityNot monotonic
2022-11-13T20:13:11.000023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6062
 
8.6%
5052
 
7.2%
7050
 
6.9%
6542
 
5.8%
7539
 
5.4%
4038
 
5.3%
4538
 
5.3%
5535
 
4.9%
8034
 
4.7%
10026
 
3.6%
Other values (84)305
42.3%
ValueCountFrequency (%)
11
 
0.1%
101
 
0.1%
206
 
0.8%
252
 
0.3%
281
 
0.1%
3013
1.8%
311
 
0.1%
3515
2.1%
361
 
0.1%
371
 
0.1%
ValueCountFrequency (%)
2551
 
0.1%
2501
 
0.1%
1901
 
0.1%
1701
 
0.1%
1651
 
0.1%
1601
 
0.1%
1503
0.4%
1441
 
0.1%
1401
 
0.1%
1351
 
0.1%

Attack
Real number (ℝ≥0)

HIGH CORRELATION

Distinct100
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.01386963
Minimum5
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.086024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q153
median74
Q395
95-th percentile125
Maximum165
Range160
Interquartile range (IQR)42

Descriptive statistics

Standard deviation28.98447528
Coefficient of variation (CV)0.3863882163
Kurtosis-0.2672204814
Mean75.01386963
Median Absolute Deviation (MAD)21
Skewness0.3084227737
Sum54085
Variance840.0998074
MonotonicityNot monotonic
2022-11-13T20:13:11.167023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5036
 
5.0%
8036
 
5.0%
10034
 
4.7%
6533
 
4.6%
6032
 
4.4%
7530
 
4.2%
5530
 
4.2%
7030
 
4.2%
8530
 
4.2%
9029
 
4.0%
Other values (90)401
55.6%
ValueCountFrequency (%)
52
 
0.3%
103
 
0.4%
151
 
0.1%
208
1.1%
221
 
0.1%
231
 
0.1%
241
 
0.1%
257
1.0%
271
 
0.1%
291
 
0.1%
ValueCountFrequency (%)
1651
 
0.1%
1602
 
0.3%
1504
 
0.6%
1471
 
0.1%
1404
 
0.6%
1355
0.7%
1342
 
0.3%
1312
 
0.3%
13012
1.7%
1291
 
0.1%

Defense
Real number (ℝ≥0)

HIGH CORRELATION

Distinct97
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.80859917
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.255023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median65
Q385
95-th percentile125
Maximum230
Range225
Interquartile range (IQR)35

Descriptive statistics

Standard deviation29.29655807
Coefficient of variation (CV)0.4137429411
Kurtosis2.485011986
Mean70.80859917
Median Absolute Deviation (MAD)17
Skewness1.121470687
Sum51053
Variance858.2883148
MonotonicityNot monotonic
2022-11-13T20:13:11.337023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7048
 
6.7%
5048
 
6.7%
6044
 
6.1%
4035
 
4.9%
6534
 
4.7%
8033
 
4.6%
4532
 
4.4%
5532
 
4.4%
9028
 
3.9%
10027
 
3.7%
Other values (87)360
49.9%
ValueCountFrequency (%)
52
 
0.3%
101
 
0.1%
154
 
0.6%
203
 
0.4%
231
 
0.1%
252
 
0.3%
281
 
0.1%
3014
1.9%
322
 
0.3%
331
 
0.1%
ValueCountFrequency (%)
2301
 
0.1%
2002
 
0.3%
1841
 
0.1%
1802
 
0.3%
1681
 
0.1%
1601
 
0.1%
1505
0.7%
1452
 
0.3%
1405
0.7%
1352
 
0.3%

Sp_Atk
Real number (ℝ≥0)

HIGH CORRELATION

Distinct94
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.73786408
Minimum10
Maximum154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.415523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q145
median65
Q390
95-th percentile125
Maximum154
Range144
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.78800523
Coefficient of variation (CV)0.4188085506
Kurtosis-0.2532792465
Mean68.73786408
Median Absolute Deviation (MAD)20
Skewness0.527096951
Sum49560
Variance828.7492449
MonotonicityNot monotonic
2022-11-13T20:13:11.495523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6049
 
6.8%
4048
 
6.7%
6541
 
5.7%
5039
 
5.4%
4533
 
4.6%
5533
 
4.6%
3529
 
4.0%
7027
 
3.7%
8526
 
3.6%
10026
 
3.6%
Other values (84)370
51.3%
ValueCountFrequency (%)
103
 
0.4%
153
 
0.4%
208
 
1.1%
231
 
0.1%
242
 
0.3%
2511
1.5%
272
 
0.3%
291
 
0.1%
3024
3.3%
311
 
0.1%
ValueCountFrequency (%)
1541
 
0.1%
1507
1.0%
1451
 
0.1%
1354
 
0.6%
1312
 
0.3%
1308
1.1%
1291
 
0.1%
1281
 
0.1%
12513
1.8%
1209
1.2%

Sp_Def
Real number (ℝ≥0)

HIGH CORRELATION

Distinct90
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.29126214
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.578022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30
Q150
median65
Q385
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)35

Descriptive statistics

Standard deviation27.01586044
Coefficient of variation (CV)0.389888416
Kurtosis2.391750774
Mean69.29126214
Median Absolute Deviation (MAD)15
Skewness1.027758955
Sum49959
Variance729.8567152
MonotonicityNot monotonic
2022-11-13T20:13:11.652523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5049
 
6.8%
5544
 
6.1%
6543
 
6.0%
8043
 
6.0%
6042
 
5.8%
7039
 
5.4%
7536
 
5.0%
4535
 
4.9%
4030
 
4.2%
9028
 
3.9%
Other values (80)332
46.0%
ValueCountFrequency (%)
205
 
0.7%
231
 
0.1%
2511
1.5%
3020
2.8%
311
 
0.1%
321
 
0.1%
331
 
0.1%
341
 
0.1%
3518
2.5%
361
 
0.1%
ValueCountFrequency (%)
2301
 
0.1%
2001
 
0.1%
1543
0.4%
1506
0.8%
1402
 
0.3%
1381
 
0.1%
1353
0.4%
1307
1.0%
1291
 
0.1%
1281
 
0.1%

Speed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct101
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.71428571
Minimum5
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.732523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q385
95-th percentile110
Maximum160
Range155
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.27792002
Coefficient of variation (CV)0.4150987829
Kurtosis-0.4461753239
Mean65.71428571
Median Absolute Deviation (MAD)20
Skewness0.2785703359
Sum47380
Variance744.0849206
MonotonicityNot monotonic
2022-11-13T20:13:11.814022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5043
 
6.0%
6043
 
6.0%
6536
 
5.0%
7036
 
5.0%
4032
 
4.4%
3032
 
4.4%
8030
 
4.2%
5529
 
4.0%
4528
 
3.9%
8527
 
3.7%
Other values (91)385
53.4%
ValueCountFrequency (%)
52
 
0.3%
103
 
0.4%
159
1.2%
2013
1.8%
221
 
0.1%
234
 
0.6%
241
 
0.1%
2510
1.4%
284
 
0.6%
293
 
0.4%
ValueCountFrequency (%)
1601
 
0.1%
1501
 
0.1%
1451
 
0.1%
1401
 
0.1%
1304
0.6%
1261
 
0.1%
1253
0.4%
1231
 
0.1%
1221
 
0.1%
1204
0.6%

Generation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.323162275
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:11.880522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.669873245
Coefficient of variation (CV)0.502495246
Kurtosis-1.272806254
Mean3.323162275
Median Absolute Deviation (MAD)2
Skewness0.006049430946
Sum2396
Variance2.788476653
MonotonicityIncreasing
2022-11-13T20:13:11.939024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5156
21.6%
1151
20.9%
3135
18.7%
4107
14.8%
2100
13.9%
672
10.0%
ValueCountFrequency (%)
1151
20.9%
2100
13.9%
3135
18.7%
4107
14.8%
5156
21.6%
672
10.0%
ValueCountFrequency (%)
672
10.0%
5156
21.6%
4107
14.8%
3135
18.7%
2100
13.9%
1151
20.9%

isLegendary
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size849.0 B
False
675 
True
 
46
ValueCountFrequency (%)
False675
93.6%
True46
 
6.4%
2022-11-13T20:13:12.004523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Color
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Blue
134 
Brown
110 
Green
79 
Red
75 
Grey
69 
Other values (5)
254 

Length

Max length6
Median length5
Mean length4.632454924
Min length3

Characters and Unicode

Total characters3340
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreen
2nd rowGreen
3rd rowGreen
4th rowRed
5th rowRed

Common Values

ValueCountFrequency (%)
Blue134
18.6%
Brown110
15.3%
Green79
11.0%
Red75
10.4%
Grey69
9.6%
Purple65
9.0%
Yellow64
8.9%
White52
 
7.2%
Pink41
 
5.7%
Black32
 
4.4%

Length

2022-11-13T20:13:12.062022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-13T20:13:12.143023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
blue134
18.6%
brown110
15.3%
green79
11.0%
red75
10.4%
grey69
9.6%
purple65
9.0%
yellow64
8.9%
white52
 
7.2%
pink41
 
5.7%
black32
 
4.4%

Most occurring characters

ValueCountFrequency (%)
e617
18.5%
l359
10.7%
r323
9.7%
B276
 
8.3%
n230
 
6.9%
u199
 
6.0%
o174
 
5.2%
w174
 
5.2%
G148
 
4.4%
P106
 
3.2%
Other values (12)734
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2619
78.4%
Uppercase Letter721
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e617
23.6%
l359
13.7%
r323
12.3%
n230
 
8.8%
u199
 
7.6%
o174
 
6.6%
w174
 
6.6%
i93
 
3.6%
d75
 
2.9%
k73
 
2.8%
Other values (6)302
11.5%
Uppercase Letter
ValueCountFrequency (%)
B276
38.3%
G148
20.5%
P106
 
14.7%
R75
 
10.4%
Y64
 
8.9%
W52
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin3340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e617
18.5%
l359
10.7%
r323
9.7%
B276
 
8.3%
n230
 
6.9%
u199
 
6.0%
o174
 
5.2%
w174
 
5.2%
G148
 
4.4%
P106
 
3.2%
Other values (12)734
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e617
18.5%
l359
10.7%
r323
9.7%
B276
 
8.3%
n230
 
6.9%
u199
 
6.0%
o174
 
5.2%
w174
 
5.2%
G148
 
4.4%
P106
 
3.2%
Other values (12)734
22.0%

hasGender
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size849.0 B
True
644 
False
77 
ValueCountFrequency (%)
True644
89.3%
False77
 
10.7%
2022-11-13T20:13:12.227024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pr_Male
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)1.1%
Missing77
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean0.5533773292
Minimum0
Maximum1
Zeros23
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:12.278022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.5
median0.5
Q30.5
95-th percentile0.875
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1999690074
Coefficient of variation (CV)0.3613610403
Kurtosis1.187097992
Mean0.5533773292
Median Absolute Deviation (MAD)0
Skewness0.08946599702
Sum356.375
Variance0.0399876039
MonotonicityNot monotonic
2022-11-13T20:13:12.338022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.5458
63.5%
0.875101
 
14.0%
023
 
3.2%
0.2522
 
3.1%
119
 
2.6%
0.7519
 
2.6%
0.1252
 
0.3%
(Missing)77
 
10.7%
ValueCountFrequency (%)
023
 
3.2%
0.1252
 
0.3%
0.2522
 
3.1%
0.5458
63.5%
0.7519
 
2.6%
0.875101
 
14.0%
119
 
2.6%
ValueCountFrequency (%)
119
 
2.6%
0.875101
 
14.0%
0.7519
 
2.6%
0.5458
63.5%
0.2522
 
3.1%
0.1252
 
0.3%
023
 
3.2%

Egg_Group_1
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
Field
169 
Monster
74 
Water_1
74 
Undiscovered
73 
Bug
66 
Other values (10)
265 

Length

Max length12
Median length10
Mean length6.703190014
Min length3

Characters and Unicode

Total characters4833
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowMonster
2nd rowMonster
3rd rowMonster
4th rowMonster
5th rowMonster

Common Values

ValueCountFrequency (%)
Field169
23.4%
Monster74
10.3%
Water_174
10.3%
Undiscovered73
10.1%
Bug66
 
9.2%
Mineral46
 
6.4%
Flying44
 
6.1%
Amorphous41
 
5.7%
Human-Like37
 
5.1%
Fairy30
 
4.2%
Other values (5)67
 
9.3%

Length

2022-11-13T20:13:12.398023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
field169
23.4%
monster74
10.3%
water_174
10.3%
undiscovered73
10.1%
bug66
 
9.2%
mineral46
 
6.4%
flying44
 
6.1%
amorphous41
 
5.7%
human-like37
 
5.1%
fairy30
 
4.2%
Other values (5)67
 
9.3%

Most occurring characters

ValueCountFrequency (%)
e575
 
11.9%
r404
 
8.4%
i400
 
8.3%
d315
 
6.5%
n284
 
5.9%
l259
 
5.4%
a253
 
5.2%
F243
 
5.0%
s242
 
5.0%
o240
 
5.0%
Other values (24)1618
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3832
79.3%
Uppercase Letter758
 
15.7%
Connector Punctuation103
 
2.1%
Decimal Number103
 
2.1%
Dash Punctuation37
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e575
15.0%
r404
10.5%
i400
10.4%
d315
8.2%
n284
7.4%
l259
 
6.8%
a253
 
6.6%
s242
 
6.3%
o240
 
6.3%
t179
 
4.7%
Other values (9)681
17.8%
Uppercase Letter
ValueCountFrequency (%)
F243
32.1%
M120
15.8%
W103
13.6%
U73
 
9.6%
B66
 
8.7%
A41
 
5.4%
H37
 
4.9%
L37
 
4.9%
G27
 
3.6%
D11
 
1.5%
Decimal Number
ValueCountFrequency (%)
174
71.8%
215
 
14.6%
314
 
13.6%
Connector Punctuation
ValueCountFrequency (%)
_103
100.0%
Dash Punctuation
ValueCountFrequency (%)
-37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4590
95.0%
Common243
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e575
12.5%
r404
 
8.8%
i400
 
8.7%
d315
 
6.9%
n284
 
6.2%
l259
 
5.6%
a253
 
5.5%
F243
 
5.3%
s242
 
5.3%
o240
 
5.2%
Other values (19)1375
30.0%
Common
ValueCountFrequency (%)
_103
42.4%
174
30.5%
-37
 
15.2%
215
 
6.2%
314
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e575
 
11.9%
r404
 
8.4%
i400
 
8.3%
d315
 
6.5%
n284
 
5.9%
l259
 
5.4%
a253
 
5.2%
F243
 
5.0%
s242
 
5.0%
o240
 
5.0%
Other values (24)1618
33.5%

Egg_Group_2
Categorical

HIGH CORRELATION
MISSING

Distinct13
Distinct (%)6.8%
Missing530
Missing (%)73.5%
Memory size5.8 KiB
Dragon
35 
Grass
32 
Field
31 
Fairy
17 
Water_3
15 
Other values (8)
61 

Length

Max length10
Median length9
Mean length6.22513089
Min length3

Characters and Unicode

Total characters1189
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowDragon
5th rowDragon

Common Values

ValueCountFrequency (%)
Dragon35
 
4.9%
Grass32
 
4.4%
Field31
 
4.3%
Fairy17
 
2.4%
Water_315
 
2.1%
Human-Like15
 
2.1%
Water_113
 
1.8%
Water_28
 
1.1%
Amorphous8
 
1.1%
Mineral8
 
1.1%
Other values (3)9
 
1.2%
(Missing)530
73.5%

Length

2022-11-13T20:13:12.465023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dragon35
18.3%
grass32
16.8%
field31
16.2%
fairy17
8.9%
water_315
7.9%
human-like15
7.9%
water_113
 
6.8%
water_28
 
4.2%
amorphous8
 
4.2%
mineral8
 
4.2%
Other values (3)9
 
4.7%

Most occurring characters

ValueCountFrequency (%)
a143
 
12.0%
r137
 
11.5%
e91
 
7.7%
i77
 
6.5%
s73
 
6.1%
n65
 
5.5%
F54
 
4.5%
o52
 
4.4%
l45
 
3.8%
g43
 
3.6%
Other values (21)409
34.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter896
75.4%
Uppercase Letter206
 
17.3%
Connector Punctuation36
 
3.0%
Decimal Number36
 
3.0%
Dash Punctuation15
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a143
16.0%
r137
15.3%
e91
10.2%
i77
8.6%
s73
8.1%
n65
7.3%
o52
 
5.8%
l45
 
5.0%
g43
 
4.8%
t37
 
4.1%
Other values (7)133
14.8%
Uppercase Letter
ValueCountFrequency (%)
F54
26.2%
W36
17.5%
D35
17.0%
G32
15.5%
L15
 
7.3%
H15
 
7.3%
M9
 
4.4%
A8
 
3.9%
B2
 
1.0%
Decimal Number
ValueCountFrequency (%)
315
41.7%
113
36.1%
28
22.2%
Connector Punctuation
ValueCountFrequency (%)
_36
100.0%
Dash Punctuation
ValueCountFrequency (%)
-15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1102
92.7%
Common87
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a143
13.0%
r137
12.4%
e91
 
8.3%
i77
 
7.0%
s73
 
6.6%
n65
 
5.9%
F54
 
4.9%
o52
 
4.7%
l45
 
4.1%
g43
 
3.9%
Other values (16)322
29.2%
Common
ValueCountFrequency (%)
_36
41.4%
-15
17.2%
315
17.2%
113
 
14.9%
28
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a143
 
12.0%
r137
 
11.5%
e91
 
7.7%
i77
 
6.5%
s73
 
6.1%
n65
 
5.5%
F54
 
4.5%
o52
 
4.4%
l45
 
3.8%
g43
 
3.6%
Other values (21)409
34.4%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size849.0 B
False
675 
True
 
46
ValueCountFrequency (%)
False675
93.6%
True46
 
6.4%
2022-11-13T20:13:12.538022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Height_m
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.144979196
Minimum0.1
Maximum14.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:12.601022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.61
median0.99
Q31.4
95-th percentile2.21
Maximum14.5
Range14.4
Interquartile range (IQR)0.79

Descriptive statistics

Standard deviation1.044368512
Coefficient of variation (CV)0.9121288112
Kurtosis50.12919007
Mean1.144979196
Median Absolute Deviation (MAD)0.41
Skewness5.508668496
Sum825.53
Variance1.09070559
MonotonicityNot monotonic
2022-11-13T20:13:12.683522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6166
 
9.2%
0.9958
 
8.0%
0.4156
 
7.8%
0.5156
 
7.8%
1.1945
 
6.2%
0.345
 
6.2%
0.7942
 
5.8%
1.541
 
5.7%
1.0936
 
5.0%
0.7134
 
4.7%
Other values (40)242
33.6%
ValueCountFrequency (%)
0.12
 
0.3%
0.213
 
1.8%
0.345
6.2%
0.4156
7.8%
0.5156
7.8%
0.6166
9.2%
0.7134
4.7%
0.7942
5.8%
0.841
 
0.1%
0.8931
4.3%
ValueCountFrequency (%)
14.51
0.1%
9.191
0.1%
8.791
0.1%
7.011
0.1%
6.911
0.1%
6.51
0.1%
6.21
0.1%
5.791
0.1%
5.411
0.1%
5.211
0.1%

Weight_kg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct398
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.77337032
Minimum0.1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:12.766523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.9
Q19.4
median28
Q361
95-th percentile220
Maximum950
Range949.9
Interquartile range (IQR)51.6

Descriptive statistics

Standard deviation89.09566682
Coefficient of variation (CV)1.569321432
Kurtosis23.8834292
Mean56.77337032
Median Absolute Deviation (MAD)21.5
Skewness4.0086685
Sum40933.6
Variance7938.037845
MonotonicityNot monotonic
2022-11-13T20:13:12.846022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59
 
1.2%
158
 
1.1%
8.58
 
1.1%
288
 
1.1%
308
 
1.1%
17
 
1.0%
27
 
1.0%
6.56
 
0.8%
96
 
0.8%
46
 
0.8%
Other values (388)648
89.9%
ValueCountFrequency (%)
0.13
0.4%
0.35
0.7%
0.52
 
0.3%
0.63
0.4%
0.82
 
0.3%
0.91
 
0.1%
17
1.0%
1.11
 
0.1%
1.24
0.6%
1.41
 
0.1%
ValueCountFrequency (%)
9501
0.1%
6831
0.1%
6501
0.1%
5501
0.1%
5051
0.1%
4601
0.1%
4301
0.1%
4201
0.1%
4001
0.1%
3981
0.1%

Catch_Rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct33
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2468793
Minimum3
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2022-11-13T20:13:12.917522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q145
median65
Q3180
95-th percentile255
Maximum255
Range252
Interquartile range (IQR)135

Descriptive statistics

Standard deviation76.57351275
Coefficient of variation (CV)0.7638493413
Kurtosis-0.6899958603
Mean100.2468793
Median Absolute Deviation (MAD)25
Skewness0.8028081219
Sum72278
Variance5863.502855
MonotonicityNot monotonic
2022-11-13T20:13:12.988523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
45223
30.9%
19069
 
9.6%
25564
 
8.9%
7557
 
7.9%
12051
 
7.1%
350
 
6.9%
6044
 
6.1%
9036
 
5.0%
3019
 
2.6%
20018
 
2.5%
Other values (23)90
12.5%
ValueCountFrequency (%)
350
 
6.9%
151
 
0.1%
2510
 
1.4%
3019
 
2.6%
351
 
0.1%
45223
30.9%
507
 
1.0%
553
 
0.4%
6044
 
6.1%
653
 
0.4%
ValueCountFrequency (%)
25564
8.9%
2356
 
0.8%
22514
 
1.9%
2202
 
0.3%
2051
 
0.1%
20018
 
2.5%
19069
9.6%
18010
 
1.4%
1702
 
0.3%
1601
 
0.1%

Body_Style
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
bipedal_tailed
158 
quadruped
135 
bipedal_tailless
109 
two_wings
63 
head_arms
39 
Other values (9)
217 

Length

Max length16
Median length9
Mean length11.61719834
Min length9

Characters and Unicode

Total characters8376
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowquadruped
2nd rowquadruped
3rd rowquadruped
4th rowbipedal_tailed
5th rowbipedal_tailed

Common Values

ValueCountFrequency (%)
bipedal_tailed158
21.9%
quadruped135
18.7%
bipedal_tailless109
15.1%
two_wings63
 
8.7%
head_arms39
 
5.4%
head_only34
 
4.7%
with_fins31
 
4.3%
insectoid30
 
4.2%
head_base30
 
4.2%
serpentine_body29
 
4.0%
Other values (4)63
 
8.7%

Length

2022-11-13T20:13:13.060023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bipedal_tailed158
21.9%
quadruped135
18.7%
bipedal_tailless109
15.1%
two_wings63
 
8.7%
head_arms39
 
5.4%
head_only34
 
4.7%
with_fins31
 
4.3%
insectoid30
 
4.2%
head_base30
 
4.2%
serpentine_body29
 
4.0%
Other values (4)63
 
8.7%

Most occurring characters

ValueCountFrequency (%)
e1009
12.0%
d889
10.6%
a871
10.4%
i809
9.7%
l750
9.0%
_556
 
6.6%
s516
 
6.2%
p446
 
5.3%
t435
 
5.2%
b354
 
4.2%
Other values (13)1741
20.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7820
93.4%
Connector Punctuation556
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1009
12.9%
d889
11.4%
a871
11.1%
i809
10.3%
l750
9.6%
s516
 
6.6%
p446
 
5.7%
t435
 
5.6%
b354
 
4.5%
u303
 
3.9%
Other values (12)1438
18.4%
Connector Punctuation
ValueCountFrequency (%)
_556
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7820
93.4%
Common556
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1009
12.9%
d889
11.4%
a871
11.1%
i809
10.3%
l750
9.6%
s516
 
6.6%
p446
 
5.7%
t435
 
5.6%
b354
 
4.5%
u303
 
3.9%
Other values (12)1438
18.4%
Common
ValueCountFrequency (%)
_556
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1009
12.0%
d889
10.6%
a871
10.4%
i809
9.7%
l750
9.0%
_556
 
6.6%
s516
 
6.2%
p446
 
5.3%
t435
 
5.2%
b354
 
4.2%
Other values (13)1741
20.8%

Interactions

2022-11-13T20:13:08.488525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.230525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.202024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.127522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:57.045024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.746023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.653024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.745524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.633522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.607523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.526523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.596023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.554024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.566022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.317522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.277523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.202522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:57.122523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.819523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.730022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.820024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.711524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.679523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.599025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.671023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.630524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.635523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.391523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.346523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.272024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:59.928524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.887024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.802023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.888023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.785024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.748525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.664524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.744523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.702023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.706024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.464024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.414022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.339523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.000524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.953524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.875025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.953522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.858024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.816523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.731525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.817023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.775023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.783023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.543024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.492023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.413522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.083523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.027024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.952524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.027025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.938024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.892024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.808025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.895522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.849523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.856024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.614523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.563024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.482024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.155024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.097524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.023024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.090524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.008523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.958024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.035024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.965524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.916024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.934022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.691024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.636524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.554523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.233023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.174022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.102024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.160523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.086023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.030023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.107523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.041523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.990523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.009022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.760025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.701523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.619523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.300523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.238023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.170022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.224024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.154522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.095024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.172021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.110025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.056523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.089522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.837524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.776523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.695024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.378525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.310524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.247523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.300024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.237522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.176024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.246023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.188524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.136523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.163024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.912024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.848022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.766022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.454022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.377023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.320024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.368022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.310022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.246024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.317022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.260022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.207524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.235024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:54.984524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.916523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.835022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.527524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.446023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.391525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.433523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.381523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.314522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.385022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.336524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.280025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.310525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.060023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.990523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.908524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.603522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.516522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.467523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.503024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.457024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.387024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.460523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.413523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.353523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:09.378022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:55.129024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.058022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:12:56.975023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:00.674024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:01.586023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:02.673524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:03.566025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:04.531523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:05.457524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:06.528024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:07.483023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-13T20:13:08.419525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-13T20:13:13.141523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-13T20:13:13.279023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-13T20:13:13.406523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-13T20:13:13.534523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-13T20:13:13.655523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-13T20:13:14.065022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-13T20:13:09.533024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-13T20:13:09.791022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-13T20:13:09.903022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-13T20:13:09.975523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

NumberNameType_1Type_2TotalHPAttackDefenseSp_AtkSp_DefSpeedGenerationisLegendaryColorhasGenderPr_MaleEgg_Group_1Egg_Group_2hasMegaEvolutionHeight_mWeight_kgCatch_RateBody_Style
01BulbasaurGrassPoison3184549496565451FalseGreenTrue0.875MonsterGrassFalse0.716.945quadruped
12IvysaurGrassPoison4056062638080601FalseGreenTrue0.875MonsterGrassFalse0.9913.045quadruped
23VenusaurGrassPoison525808283100100801FalseGreenTrue0.875MonsterGrassTrue2.01100.045quadruped
34CharmanderFireNaN3093952436050651FalseRedTrue0.875MonsterDragonFalse0.618.545bipedal_tailed
45CharmeleonFireNaN4055864588065801FalseRedTrue0.875MonsterDragonFalse1.0919.045bipedal_tailed
56CharizardFireFlying534788478109851001FalseRedTrue0.875MonsterDragonTrue1.7090.545bipedal_tailed
67SquirtleWaterNaN3144448655064431FalseBlueTrue0.875MonsterWater_1False0.519.045bipedal_tailed
78WartortleWaterNaN4055963806580581FalseBlueTrue0.875MonsterWater_1False0.9922.545bipedal_tailed
89BlastoiseWaterNaN530798310085105781FalseBlueTrue0.875MonsterWater_1True1.6085.545bipedal_tailed
910CaterpieBugNaN1954530352020451FalseGreenTrue0.500BugNaNFalse0.302.9255insectoid

Last rows

NumberNameType_1Type_2TotalHPAttackDefenseSp_AtkSp_DefSpeedGenerationisLegendaryColorhasGenderPr_MaleEgg_Group_1Egg_Group_2hasMegaEvolutionHeight_mWeight_kgCatch_RateBody_Style
711712BergmiteIceNaN3045569853235286FalseBlueTrue0.5MonsterNaNFalse0.9999.5190quadruped
712713AvaluggIceNaN514951171844446286FalseBlueTrue0.5MonsterNaNFalse2.01505.055quadruped
713714NoibatFlyingDragon2454030354540556FalsePurpleTrue0.5FlyingNaNFalse0.518.0190two_wings
714715NoivernFlyingDragon53585708097801236FalsePurpleTrue0.5FlyingNaNFalse1.5085.045two_wings
715716XerneasFairyNaN6801261319513198996TrueBlueFalseNaNUndiscoveredNaNFalse3.00215.045quadruped
716717YveltalDarkFlying6801261319513198996TrueRedFalseNaNUndiscoveredNaNFalse5.79203.045two_wings
717718ZygardeDragonGround6001081001218195956TrueGreenFalseNaNUndiscoveredNaNFalse5.00305.03serpentine_body
718719DiancieRockFairy60050100150100150506TruePinkFalseNaNUndiscoveredNaNTrue0.718.83head_arms
719720HoopaPsychicGhost6008011060150130706TruePurpleFalseNaNUndiscoveredNaNFalse0.519.03head_only
720721VolcanionFireWater6008011012013090706TrueBrownFalseNaNUndiscoveredNaNFalse1.70195.03quadruped